In this work, multiband features are used to detect seizure with feedforward neural network (FfNN). The EEG signal is segmented into epochs of short duration ...
In this work, multiband features are used to detect seizure with feedforward neural network (FfNN). The EEG signal is segmented into epochs of short duration ...
The experimental results show the superiority of the FfNN method compared to the recently developed algorithms in seizure detection. Electroencephalography ...
An EMD-chaos based approach is proposed to discriminate EEG signals corresponding to healthy persons, and epileptic patients during seizure-free intervals ...
EEG signals are the most promising brain signals for ES analysis and recognition, particularly for recognizing the abnormality of the brain due to its painless ...
This paper presents a novel method for automatic epileptic seizure detection, which uses approximate entropy features derived from multiwavelet transform.
Aug 18, 2020 · The selected features are used to classify EEG signals into seizure and nonseizure categories using a feedforward neural network (FfNN). The ...
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An epileptologist visually inspects the long-term multi-channel EEG signals to identify the electrodes with the possible SOZ that must be resected in order to ...
... using DWT-based ApEn and artificial neural network ... Epileptic Seizure Detection from EEG Signals Using Multiband Features with Feedforward Neural Network.
This study investigated the well-performed EEG-based ES detection method by decomposing EEG signals. Specifically, empirical mode decomposition (EMD) decomposes ...